Overview

Dataset statistics

Number of variables14
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory109.5 KiB
Average record size in memory112.1 B

Variable types

Numeric13
Categorical1

Alerts

df_index is highly correlated with F8High correlation
F8 is highly correlated with df_indexHigh correlation
df_index is highly correlated with F8High correlation
F8 is highly correlated with df_indexHigh correlation
df_index is highly correlated with F8High correlation
F8 is highly correlated with df_indexHigh correlation
df_index is highly correlated with F8High correlation
F8 is highly correlated with df_indexHigh correlation
df_index is uniformly distributed Uniform
df_index has unique values Unique

Reproduction

Analysis started2022-04-27 10:03:34.827894
Analysis finished2022-04-27 10:04:19.937450
Duration45.11 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean513.675
Minimum0
Maximum1022
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-04-27T13:04:20.087483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile51.95
Q1255.75
median516.5
Q3771.25
95-th percentile972.05
Maximum1022
Range1022
Interquartile range (IQR)515.5

Descriptive statistics

Standard deviation296.1646956
Coefficient of variation (CV)0.5765604625
Kurtosis-1.207084163
Mean513.675
Median Absolute Deviation (MAD)257.5
Skewness-0.01323768751
Sum513675
Variance87713.5269
MonotonicityStrictly increasing
2022-04-27T13:04:20.307536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
6911
 
0.1%
6781
 
0.1%
6791
 
0.1%
6801
 
0.1%
6811
 
0.1%
6821
 
0.1%
6831
 
0.1%
6841
 
0.1%
6851
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
ValueCountFrequency (%)
10221
0.1%
10211
0.1%
10201
0.1%
10191
0.1%
10181
0.1%
10171
0.1%
10161
0.1%
10151
0.1%
10141
0.1%
10131
0.1%

T1
Real number (ℝ≥0)

Distinct992
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.936001659
Minimum0.3894026052
Maximum5.591741599
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-04-27T13:04:20.489617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.3894026052
5-th percentile1.509286438
Q12.319282787
median2.911393929
Q33.557017544
95-th percentile4.475298308
Maximum5.591741599
Range5.202338994
Interquartile range (IQR)1.237734757

Descriptive statistics

Standard deviation0.9141434779
Coefficient of variation (CV)0.3113565944
Kurtosis-0.2052103396
Mean2.936001659
Median Absolute Deviation (MAD)0.6289903169
Skewness0.08425090127
Sum2936.001659
Variance0.8356582982
MonotonicityNot monotonic
2022-04-27T13:04:20.710968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.8571428574
 
0.4%
3.5570175442
 
0.2%
1.5981735162
 
0.2%
2.8773584912
 
0.2%
2.9196778362
 
0.2%
4.0291262142
 
0.2%
3.476583451
 
0.1%
1.9342290841
 
0.1%
4.5941192841
 
0.1%
1.0853989881
 
0.1%
Other values (982)982
98.2%
ValueCountFrequency (%)
0.38940260521
0.1%
0.46334203591
0.1%
0.54739100741
0.1%
0.55150849821
0.1%
0.59678289571
0.1%
0.74922322071
0.1%
0.82701571581
0.1%
0.85738076681
0.1%
0.8754576311
0.1%
0.9278406951
0.1%
ValueCountFrequency (%)
5.5917415991
0.1%
5.4555661781
0.1%
5.4529594311
0.1%
5.4251394741
0.1%
5.3141436851
0.1%
5.2958419931
0.1%
5.2588945781
0.1%
5.2104424391
0.1%
5.1203684781
0.1%
5.1107805531
0.1%

F1
Real number (ℝ≥0)

Distinct991
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1975.285938
Minimum1731.764635
Maximum2207.773481
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-04-27T13:04:20.851264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1731.764635
5-th percentile1854.001819
Q11923.628661
median1977.321002
Q32021.238927
95-th percentile2099.706618
Maximum2207.773481
Range476.0088455
Interquartile range (IQR)97.6102668

Descriptive statistics

Standard deviation73.78834229
Coefficient of variation (CV)0.03735577765
Kurtosis0.07215238705
Mean1975.285938
Median Absolute Deviation (MAD)48.93898282
Skewness0.06496909214
Sum1975285.938
Variance5444.719457
MonotonicityNot monotonic
2022-04-27T13:04:21.031331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20306
 
0.6%
19102
 
0.2%
20002
 
0.2%
19502
 
0.2%
18802
 
0.2%
1866.6055231
 
0.1%
1911.8195171
 
0.1%
1915.6161531
 
0.1%
2014.8254831
 
0.1%
2025.6482291
 
0.1%
Other values (981)981
98.1%
ValueCountFrequency (%)
1731.7646351
0.1%
1740.6574961
0.1%
1784.4822451
0.1%
1786.0356361
0.1%
1797.6482341
0.1%
1801.9406951
0.1%
1804.840231
0.1%
1805.7359991
0.1%
1807.595911
0.1%
1810.3577741
0.1%
ValueCountFrequency (%)
2207.7734811
0.1%
2192.7387831
0.1%
2192.2976371
0.1%
2184.49321
0.1%
2182.7518221
0.1%
2172.2467961
0.1%
2170.3423631
0.1%
2161.5652161
0.1%
2160.7514391
0.1%
21601
0.1%

F2
Real number (ℝ≥0)

Distinct997
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.447920876
Minimum1.234572449
Maximum7.408782478
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-04-27T13:04:21.216349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.234572449
5-th percentile5.202675982
Q16.218144611
median6.606294504
Q36.865247098
95-th percentile7.148701752
Maximum7.408782478
Range6.174210029
Interquartile range (IQR)0.6471024866

Descriptive statistics

Standard deviation0.6879506699
Coefficient of variation (CV)0.1066934107
Kurtosis10.73803985
Mean6.447920876
Median Absolute Deviation (MAD)0.3069876325
Skewness-2.579082338
Sum6447.920876
Variance0.4732761243
MonotonicityNot monotonic
2022-04-27T13:04:21.396391image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.6062945044
 
0.4%
6.16350341
 
0.1%
6.4902879661
 
0.1%
6.7541707141
 
0.1%
4.6415183031
 
0.1%
6.5185890431
 
0.1%
6.4424952041
 
0.1%
6.8652606661
 
0.1%
6.5423754381
 
0.1%
7.0032213021
 
0.1%
Other values (987)987
98.7%
ValueCountFrequency (%)
1.2345724491
0.1%
1.675067281
0.1%
2.3966408431
0.1%
2.5106509511
0.1%
3.1255314381
0.1%
3.2033398851
0.1%
3.3260067651
0.1%
3.356027621
0.1%
3.4823038741
0.1%
3.4845720471
0.1%
ValueCountFrequency (%)
7.4087824781
0.1%
7.3957216091
0.1%
7.3877692451
0.1%
7.3712862671
0.1%
7.3608009581
0.1%
7.3442608651
0.1%
7.3421470691
0.1%
7.3415930221
0.1%
7.3216394081
0.1%
7.3151624791
0.1%

F3
Real number (ℝ≥0)

Distinct983
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.482557
Minimum30
Maximum191.0530037
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-04-27T13:04:21.603582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile60.96935704
Q192.45558199
median110.5892849
Q3129.6155891
95-th percentile154.9223459
Maximum191.0530037
Range161.0530037
Interquartile range (IQR)37.1600071

Descriptive statistics

Standard deviation27.7252434
Coefficient of variation (CV)0.2509467934
Kurtosis-0.05538125839
Mean110.482557
Median Absolute Deviation (MAD)18.74493289
Skewness-0.1295630124
Sum110482.557
Variance768.6891218
MonotonicityNot monotonic
2022-04-27T13:04:21.783834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12912
 
1.2%
111.867
 
0.7%
301
 
0.1%
110.86014581
 
0.1%
86.510594731
 
0.1%
55.357622931
 
0.1%
86.964800541
 
0.1%
122.89635761
 
0.1%
78.361530641
 
0.1%
118.70128771
 
0.1%
Other values (973)973
97.3%
ValueCountFrequency (%)
301
0.1%
32.019221561
0.1%
33.62418651
0.1%
35.599697411
0.1%
35.620904361
0.1%
38.668500331
0.1%
40.304805691
0.1%
41.429138991
0.1%
41.886277081
0.1%
43.132583111
0.1%
ValueCountFrequency (%)
191.05300371
0.1%
190.31810721
0.1%
188.04989791
0.1%
181.82844781
0.1%
181.03280921
0.1%
179.64596171
0.1%
176.9188461
0.1%
173.25432621
0.1%
172.57053851
0.1%
171.53632751
0.1%

F4
Real number (ℝ≥0)

Distinct982
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.24460923
Minimum14.2549855
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-04-27T13:04:21.979660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum14.2549855
5-th percentile18.37916503
Q120.60832147
median22.23054547
Q323.96181796
95-th percentile26.16684927
Maximum33
Range18.7450145
Interquartile range (IQR)3.353496482

Descriptive statistics

Standard deviation2.405650189
Coefficient of variation (CV)0.1081453112
Kurtosis0.06530611552
Mean22.24460923
Median Absolute Deviation (MAD)1.679983131
Skewness0.05686497497
Sum22244.60923
Variance5.787152831
MonotonicityNot monotonic
2022-04-27T13:04:22.631414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.2512
 
1.2%
22.267857148
 
0.8%
24.913132571
 
0.1%
26.966870241
 
0.1%
26.172367241
 
0.1%
16.993851331
 
0.1%
18.309487911
 
0.1%
19.545168691
 
0.1%
21.118524731
 
0.1%
21.797640441
 
0.1%
Other values (972)972
97.2%
ValueCountFrequency (%)
14.25498551
0.1%
15.69589381
0.1%
15.881667511
0.1%
16.048978331
0.1%
16.391594731
0.1%
16.667531791
0.1%
16.704107981
0.1%
16.715437331
0.1%
16.779820091
0.1%
16.848536321
0.1%
ValueCountFrequency (%)
331
0.1%
28.955094371
0.1%
28.907470031
0.1%
28.848901621
0.1%
28.620115791
0.1%
28.324968021
0.1%
27.920843271
0.1%
27.813183181
0.1%
27.707681741
0.1%
27.633742481
0.1%

F5
Real number (ℝ≥0)

Distinct981
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean285.7807054
Minimum100
Maximum413.2734182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-04-27T13:04:22.860986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile220.2219357
Q1258.9510659
median285.8539604
Q3313.0291261
95-th percentile351.936617
Maximum413.2734182
Range313.2734182
Interquartile range (IQR)54.07806023

Descriptive statistics

Standard deviation40.96939674
Coefficient of variation (CV)0.1433595619
Kurtosis0.1922065832
Mean285.7807054
Median Absolute Deviation (MAD)27.07381442
Skewness-0.06699407063
Sum285780.7054
Variance1678.491469
MonotonicityNot monotonic
2022-04-27T13:04:23.073449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30012
 
1.2%
284.61538469
 
0.9%
345.2145911
 
0.1%
290.17373341
 
0.1%
290.2169231
 
0.1%
257.6796691
 
0.1%
351.9696371
 
0.1%
288.91953451
 
0.1%
246.47302581
 
0.1%
212.02122451
 
0.1%
Other values (971)971
97.1%
ValueCountFrequency (%)
1001
0.1%
160.25584291
0.1%
173.48491991
0.1%
173.9739071
0.1%
179.37439141
0.1%
186.50861281
0.1%
187.56235291
0.1%
187.97796461
0.1%
188.91867361
0.1%
189.20839211
0.1%
ValueCountFrequency (%)
413.27341821
0.1%
403.65286091
0.1%
397.15129091
0.1%
396.89822221
0.1%
386.06799181
0.1%
385.89477051
0.1%
385.69779921
0.1%
383.93840351
0.1%
382.7598081
0.1%
381.85772451
0.1%

F6
Real number (ℝ≥0)

Distinct982
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.910909132
Minimum0.4723383549
Maximum7.244614842
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-04-27T13:04:23.330005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.4723383549
5-th percentile4.154426976
Q15.588016535
median6.114370898
Q36.540349094
95-th percentile6.885493629
Maximum7.244614842
Range6.772276487
Interquartile range (IQR)0.9523325598

Descriptive statistics

Standard deviation0.9044039058
Coefficient of variation (CV)0.1530058889
Kurtosis5.247449248
Mean5.910909132
Median Absolute Deviation (MAD)0.4564491244
Skewness-1.890866543
Sum5910.909132
Variance0.8179464248
MonotonicityNot monotonic
2022-04-27T13:04:23.521443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.3518581339
 
0.9%
6.1548580945
 
0.5%
5.9427993754
 
0.4%
6.9186952194
 
0.4%
6.4447173691
 
0.1%
5.4785455391
 
0.1%
5.5253902281
 
0.1%
6.1941467871
 
0.1%
4.3381709391
 
0.1%
6.0579641421
 
0.1%
Other values (972)972
97.2%
ValueCountFrequency (%)
0.47233835491
0.1%
0.98132995411
0.1%
1.0626715921
0.1%
1.6290529541
0.1%
2.0514606091
0.1%
2.0777767731
0.1%
2.0912903181
0.1%
2.2471059391
0.1%
2.3071947631
0.1%
2.5092109091
0.1%
ValueCountFrequency (%)
7.2446148421
0.1%
7.2385201231
0.1%
7.1642098961
0.1%
7.1621585881
0.1%
7.1224443241
0.1%
7.1159599711
0.1%
7.113340051
0.1%
7.0760844911
0.1%
7.0752519781
0.1%
7.0720933221
0.1%

T2
Real number (ℝ≥0)

Distinct982
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.31591073
Minimum64.05406056
Maximum82.68205104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-04-27T13:04:23.865621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum64.05406056
5-th percentile68.46023975
Q171.23334295
median73.2592296
Q375.32163702
95-th percentile78.86129837
Maximum82.68205104
Range18.62799048
Interquartile range (IQR)4.088294069

Descriptive statistics

Standard deviation3.104660884
Coefficient of variation (CV)0.04234634547
Kurtosis-0.1305565773
Mean73.31591073
Median Absolute Deviation (MAD)2.050742738
Skewness0.1351730657
Sum73315.91073
Variance9.638919205
MonotonicityNot monotonic
2022-04-27T13:04:24.177111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
709
 
0.9%
73.333333335
 
0.5%
754
 
0.4%
784
 
0.4%
78.895825931
 
0.1%
77.181810731
 
0.1%
72.193575551
 
0.1%
75.134601751
 
0.1%
69.047399131
 
0.1%
70.108416241
 
0.1%
Other values (972)972
97.2%
ValueCountFrequency (%)
64.054060561
0.1%
64.69640041
0.1%
65.553335911
0.1%
65.793844971
0.1%
65.97999051
0.1%
66.052746621
0.1%
66.22123381
0.1%
66.265289961
0.1%
66.421070061
0.1%
66.482382761
0.1%
ValueCountFrequency (%)
82.682051041
0.1%
82.525772991
0.1%
82.237599861
0.1%
81.594750011
0.1%
81.417125941
0.1%
81.203146721
0.1%
81.175230551
0.1%
81.053292821
0.1%
80.97095951
0.1%
80.803221761
0.1%

T3
Real number (ℝ≥0)

Distinct982
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2461.358931
Minimum1143.210334
Maximum3725.19076
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-04-27T13:04:24.383024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1143.210334
5-th percentile1687.298269
Q12135.886086
median2455.974462
Q32755.169485
95-th percentile3268.454069
Maximum3725.19076
Range2581.980426
Interquartile range (IQR)619.2833993

Descriptive statistics

Standard deviation471.1649946
Coefficient of variation (CV)0.1914247405
Kurtosis-0.09982030573
Mean2461.358931
Median Absolute Deviation (MAD)308.9855342
Skewness0.05453090432
Sum2461358.931
Variance221996.4522
MonotonicityNot monotonic
2022-04-27T13:04:24.589380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30009
 
0.9%
2455.5555565
 
0.5%
18004
 
0.4%
20004
 
0.4%
3409.5257411
 
0.1%
2435.0754091
 
0.1%
2066.5674581
 
0.1%
2431.9408021
 
0.1%
2039.0222351
 
0.1%
2461.4551071
 
0.1%
Other values (972)972
97.2%
ValueCountFrequency (%)
1143.2103341
0.1%
1145.0657571
0.1%
1188.3074181
0.1%
1250.3928021
0.1%
1264.3106821
0.1%
1269.7282031
0.1%
1325.3017471
0.1%
1337.4246921
0.1%
1337.7483171
0.1%
1348.2882511
0.1%
ValueCountFrequency (%)
3725.190761
0.1%
3705.6725231
0.1%
3694.2980441
0.1%
3693.6765311
0.1%
3689.2236811
0.1%
3660.450211
0.1%
3656.1583631
0.1%
3654.4343591
0.1%
3636.8929921
0.1%
3628.8771931
0.1%

F7
Real number (ℝ≥0)

Distinct981
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218.1970143
Minimum41.04827795
Maximum414.5906284
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-04-27T13:04:24.739518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum41.04827795
5-th percentile120
Q1179.8122129
median219.0627514
Q3257.0866232
95-th percentile313.2203523
Maximum414.5906284
Range373.5423504
Interquartile range (IQR)77.2744103

Descriptive statistics

Standard deviation59.22548931
Coefficient of variation (CV)0.2714312545
Kurtosis-0.1275646844
Mean218.1970143
Median Absolute Deviation (MAD)38.67913221
Skewness0.02722932509
Sum218197.0143
Variance3507.658584
MonotonicityNot monotonic
2022-04-27T13:04:24.919350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22014
 
1.4%
3004
 
0.4%
1204
 
0.4%
197.12606671
 
0.1%
175.76791891
 
0.1%
259.9171291
 
0.1%
127.18223841
 
0.1%
189.29941571
 
0.1%
246.26712391
 
0.1%
147.83187881
 
0.1%
Other values (971)971
97.1%
ValueCountFrequency (%)
41.048277951
0.1%
53.548916361
0.1%
63.685698331
0.1%
64.524179641
0.1%
72.530873381
0.1%
74.097865691
0.1%
75.831811691
0.1%
76.023074651
0.1%
82.583161431
0.1%
84.49012931
0.1%
ValueCountFrequency (%)
414.59062841
0.1%
402.16380921
0.1%
386.90343141
0.1%
383.66340051
0.1%
378.75687881
0.1%
359.05221981
0.1%
356.92597451
0.1%
355.75869681
0.1%
354.95808781
0.1%
350.67212741
0.1%

F8
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0.0
502 
1.0
498 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0502
50.2%
1.0498
49.8%

Length

2022-04-27T13:04:25.139405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-27T13:04:25.314125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0502
50.2%
1.0498
49.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

F9
Real number (ℝ≥0)

Distinct966
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.910569759
Minimum0.0376389367
Maximum14.44052188
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-04-27T13:04:25.540468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.0376389367
5-th percentile2.799946308
Q15.080695106
median6.929392696
Q38.588839371
95-th percentile11.36172987
Maximum14.44052188
Range14.40288294
Interquartile range (IQR)3.508144266

Descriptive statistics

Standard deviation2.564005692
Coefficient of variation (CV)0.371026671
Kurtosis-0.03444220563
Mean6.910569759
Median Absolute Deviation (MAD)1.762141678
Skewness0.1080951249
Sum6910.569759
Variance6.574125191
MonotonicityNot monotonic
2022-04-27T13:04:25.790514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78
 
0.8%
98
 
0.8%
108
 
0.8%
58
 
0.8%
47
 
0.7%
10.853242141
 
0.1%
4.0631830411
 
0.1%
4.3952538481
 
0.1%
9.1278818941
 
0.1%
8.1013427661
 
0.1%
Other values (956)956
95.6%
ValueCountFrequency (%)
0.03763893671
0.1%
0.1450341431
0.1%
0.23966031781
0.1%
0.26875555761
0.1%
0.30572260311
0.1%
0.31081533811
0.1%
0.39019069581
0.1%
0.57183487191
0.1%
0.73025986951
0.1%
0.9394624741
0.1%
ValueCountFrequency (%)
14.440521881
0.1%
14.376451281
0.1%
14.051382821
0.1%
14.03321531
0.1%
13.73240441
0.1%
13.653987051
0.1%
13.57192071
0.1%
13.48494511
0.1%
13.378772111
0.1%
13.374854781
0.1%

F10
Real number (ℝ≥0)

Distinct965
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.30408905
Minimum20.57163333
Maximum92.96349195
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-04-27T13:04:25.984480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20.57163333
5-th percentile36.76644821
Q149.86831666
median57.51647228
Q364.93089601
95-th percentile76.95531926
Maximum92.96349195
Range72.39185862
Interquartile range (IQR)15.06257935

Descriptive statistics

Standard deviation11.85343161
Coefficient of variation (CV)0.2068514099
Kurtosis0.1065646173
Mean57.30408905
Median Absolute Deviation (MAD)7.536514316
Skewness-0.1185634015
Sum57304.08905
Variance140.503841
MonotonicityNot monotonic
2022-04-27T13:04:26.159501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5710
 
1.0%
7010
 
1.0%
6010
 
1.0%
479
 
0.9%
54.193898011
 
0.1%
61.764554551
 
0.1%
47.042864391
 
0.1%
54.495993571
 
0.1%
65.497143751
 
0.1%
72.813177461
 
0.1%
Other values (955)955
95.5%
ValueCountFrequency (%)
20.571633331
0.1%
23.143400561
0.1%
23.898195771
0.1%
24.283522951
0.1%
24.860511321
0.1%
25.682856291
0.1%
26.559894371
0.1%
26.647154491
0.1%
27.019374441
0.1%
27.272928011
0.1%
ValueCountFrequency (%)
92.963491951
0.1%
92.042139381
0.1%
89.876615511
0.1%
88.807647151
0.1%
88.072489511
0.1%
86.012427011
0.1%
85.987173791
0.1%
85.660996191
0.1%
84.840887541
0.1%
84.669110781
0.1%

Interactions

2022-04-27T13:04:16.660515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:39.414746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:45.662464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:48.387204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:50.878055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:53.481452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:56.622008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:59.180670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:01.641009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:04.255633image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:07.600224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:10.159842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:13.260338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:16.829548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:40.245287image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:45.912526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:48.563788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:51.063106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:53.702106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:56.812519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:59.386160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:01.872094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:04.527121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:07.750387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:10.344886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:13.454989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:17.032878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:40.926161image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:46.077566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:48.763752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:51.203133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:53.902133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:56.967559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:59.557389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:02.087145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:05.112093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:07.930430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:10.516269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:13.742914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:17.182909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:41.661264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:46.239385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:48.933796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:51.418206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:54.097183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:57.117592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:59.706897image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:02.255500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:05.342140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:08.110470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:10.852224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:13.985886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:17.390167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:42.370106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:46.389418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:49.083829image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:51.614475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:54.307229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:57.332729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:59.886922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:02.409579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:05.564452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:08.305543image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:11.162301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:14.217852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:17.610057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:43.028887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:46.572011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:49.249957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:51.814180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:54.520021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:57.546639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:00.142795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:02.645945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:05.803570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:08.482061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:11.427359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:14.392892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:17.800205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:43.741604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:47.141031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:49.466400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:52.019228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:54.780530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:57.735669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:00.317832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:02.814902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:05.973606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:08.691629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:11.664210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:15.131135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:17.976455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:44.441785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:47.306610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:49.695980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:52.223200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:54.955576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:57.930717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:00.504016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:03.031984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:06.188637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:08.936779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:11.877528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:15.327227image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:18.156522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:44.681254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:47.457127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:49.861017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:52.403407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:55.180610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:58.110773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-04-27T13:04:12.132594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-04-27T13:04:18.393621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:44.881456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:47.631059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:50.066067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:52.639329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:55.375676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-04-27T13:04:00.879377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:03.389777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:06.662879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:09.328601image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:12.448421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:15.803922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:18.559632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:45.076481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:47.806087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:50.307338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:52.834382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-04-27T13:04:01.054444image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:03.586263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-04-27T13:04:12.709643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:15.978993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:18.802384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:45.272059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:48.033237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:50.493731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:52.999396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:56.219327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:58.739127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:01.224481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:03.850562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:07.181847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:09.689325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:12.914395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:16.239508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:19.062440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:45.458244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:48.195368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:50.658007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:53.225252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:56.451969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:03:58.950171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:01.449510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:04.025601image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:07.393648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:09.909786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:13.084436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-27T13:04:16.412502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-04-27T13:04:26.395093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-27T13:04:26.596861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-27T13:04:26.794213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-27T13:04:27.038237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-27T13:04:19.386413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-27T13:04:19.748212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexT1F1F2F3F4F5F6T2T3F7F8F9F10
00.01.8571432030.06.60629530.0022.267857100.0000005.35185870.03000.0220.00.04.057.0
11.01.8571432030.06.60629550.0023.750000284.6153855.35185870.03000.0220.00.04.060.0
22.01.8571432030.06.60629549.9033.000000284.6153855.35185870.03000.0220.00.04.070.0
33.01.8571432030.06.606295129.0021.250000300.0000005.35185870.03000.0220.00.05.047.0
44.02.7713312030.06.625392111.8622.267857284.6153855.35185870.03000.0220.00.05.057.0
55.02.7679182000.06.618739111.8622.267857284.6153855.35185870.03000.0220.00.05.060.0
66.02.5696201910.06.694562111.8622.267857284.6153855.35185870.03000.0220.00.05.070.0
77.02.5614751900.06.284134111.8622.267857284.6153855.94279975.01800.0120.00.07.047.0
88.03.5570181930.06.791221129.0021.250000300.0000005.94279975.01800.0120.00.07.057.0
99.03.5323382100.07.259820129.0021.250000300.0000006.91869578.02000.0300.00.07.060.0

Last rows

df_indexT1F1F2F3F4F5F6T2T3F7F8F9F10
9901013.02.3103941931.1468876.31898796.74978222.146487214.8277274.04730178.1436091939.30755087.2701391.07.68334662.785021
9911014.01.6462352014.7725476.735857102.97990621.073367271.4908436.42352079.1544692518.516089232.4282141.05.04850359.837798
9921015.02.8065631872.8646606.904770146.19919421.559290313.9004866.68540472.8155522443.482888307.2651721.05.24044852.044507
9931016.03.7458621914.6294246.524566110.97910025.922635309.7963886.44471176.0305552466.925422152.1847201.08.05702047.067229
9941017.02.7587272000.5061416.841150143.02185921.379518273.8526794.19125967.6337523102.539548229.7803721.08.73659260.277805
9951018.02.2713461952.0879026.81767386.99218320.123249324.7745765.34805373.0909612387.292495125.0076691.09.07638047.019770
9961019.03.4440222050.0891716.099719145.98197819.599769254.2154015.86266772.9208272360.392784117.7300991.010.56561453.750790
9971020.03.2806041972.3728656.035090110.53347723.957502248.4230476.60819374.7343442662.906040236.6067641.04.16115467.629684
9981021.03.7053512066.7997736.609990141.39796319.246945275.7798406.46531774.0427082071.715856197.1260671.06.31320158.261074
9991022.03.8080201890.4134686.036238129.18341627.474763300.9527086.63298774.3097042856.328932194.7543421.06.07890277.434468